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Over the past few years, Convolutional neural networks (ConvNets) is emerging as computer vision discipline within deep learning. ConvNets is a key strategy for addressing computer vision problems, yet the theories behind their effectiveness in the processing are not yet fully understood. ConvNets have attained a state of the art-performances on various datasets for computer vision tasks such as remote sensing images scene classification, face recognition, and object detection. This is attributed to their effectiveness in image feature processing. This work reviews the major advances on ConvNets for effective processing in computer vision from some dimensions which include, convolutional layer design configurations, pooling layer strategies, network activation functions, loss functions, network regularization techniques, and ConvNet optimization methods. Further, this works surveys the application of ConvNets on three computer vision tasks,  i.e.  remote sensing images scene recognition, face recognition, and object detection to demonstrate the effectiveness of convNets in image feature processing. Additionally, datasets for evaluation and benchmarking purposes with the aforementioned computer vision tasks are briefly discussed.

Effective Processing of Convolutional Neural Networks for Computer Vision: A Tutorial and Survey